A fuzzy gap analysis model for evaluating the performance of engineering consultants

Abstract Evaluating the performance of Engineering Consultants (ECs) is a major challenge as the process involves a number of subjective elements. However, as performance is an important dimension for prequalifying and selecting ECs, it would be helpful if relevant data can be solicited from various clients. The diversity in the Consultant Performance Evaluation (CPE) criteria and the interpretation of performance levels amongst various assessors would undoubtedly undermine the reliability and transparency of CPE. In this paper, a fuzzy gap analysis model is proposed to improve the practice of CPE. The study begins by identifying a list of CPE criteria and their corresponding quantitative indicators for the design stage of a project. Considering the performance levels for each of the quantitative indicators could be interpreted by assessors differently, an empirical survey is conducted to identify a range of possible values within each of the ‘poor’, ‘average’, ‘good’, ‘very good’ and ‘excellent’ performance levels. The fuzzy gap analysis CPE model is proposed to facilitate assessors to compare the client's expectation and actual service quality of an EC. The model proposed in this paper is not only novel but could also improve the fairness and transparency of this type of decision under an increasingly collaborative environment.

[1]  L. Berry,et al.  Emerging perspectives on services marketing , 1983 .

[2]  P. Holroyd,et al.  Developing expert systems for management applications , 1985 .

[3]  Kenneth C. Giberson.  Competitive Bidding for A/E Services? Why Not? , 1980 .

[4]  S. Thomas Ng,et al.  Fuzzy membership functions of procurement selection criteria , 2002 .

[5]  N Lalani,et al.  SELECTING THE RIGHT CONSULTANT TEAM , 1996 .

[6]  Mike Bresnen,et al.  Construction industry clients: A survey of their attributes and project management practices , 1991 .

[7]  Lai-kit. Chow,et al.  Incorporating fuzzy membership functions and gap analysis concept intoperformance evaluation of engineering consultants: Hong Kong study , 2005 .

[8]  Gilbert Ja.  Selecting the right consultant. , 1999 .

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  William R. King,et al.  Project Management Handbook , 1983 .

[11]  V. V. S. Sarma,et al.  Estimation of fuzzy memberships from histograms , 1985, Inf. Sci..

[12]  Yean Yng Ling,et al.  Model for Predicting Performance of Architects and Engineers , 2002 .

[13]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[14]  Andrew Edkins,et al.  Towards total project quality: a gap analysis approach , 1998 .

[15]  B. N. Baker,et al.  Factors Affecting Project Success , 2008 .

[16]  Xxyyzz.  Consulting Engineering : A Guide for the Engagement of Engineering Services , 1965 .

[17]  Franco K.T. Cheung,et al.  Multi-criteria evaluation model for the selection of architectural consultants , 2002 .

[18]  A. Parasuraman,et al.  A Conceptual Model of Service Quality and Its Implications for Future Research , 1985 .

[19]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[20]  Martin Skitmore,et al.  Analysing a consultant's competitiveness in two‐envelope fee tendering , 2001 .

[21]  S. Thomas Ng,et al.  Framework for evaluating the performance of engineering consultants , 2004 .

[22]  Derek S. Drew,et al.  Correcting the fee-technical score variability imbalance in two-envelope fee tendering , 2002 .

[23]  Fletcher Griffis,et al.  QUALIFICATIONS-BASED SELECTION OF PROFESSIONAL A/E SERVICES , 2004 .

[24]  Lk Chow,et al.  Performance-based evaluation for engineering consultants - a study on the assessment criteria , 2003 .

[25]  Gilbert A. Churchill,et al.  An Investigation into the Determinants of Customer Satisfaction , 1982 .

[26]  Derek S. Drew,et al.  Balancing fee and quality in two envelope fee bidding , 2004 .

[27]  Kenneth L. Bernhardt,et al.  Comments on Christian Grönroos' Strategic management and marketing in the service sector , 1984 .

[28]  David G. Ullman,et al.  Robust decision-making for engineering design , 2001 .

[29]  Robert R. McBride,et al.  COMPETITIVE BIDDING FOR ENGINEERING SERVICES , 1987 .

[30]  Roy Pilcher,et al.  Project cost control in construction , 1985 .

[31]  W. B. Ledbetter,et al.  Causes of Quality Deviations in Design and Construction , 1992 .

[32]  Yean Yng Ling,et al.  A conceptual model for selection of architects by project managers in Singapore , 2003 .

[33]  Tst Ng,et al.  A framework for evaluating the performance of engineering consultants in Hong Kong , 2004 .

[34]  H. Trussell,et al.  Constructing membership functions using statistical data , 1986 .